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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal

Zepa Yang¹, Insung Choi¹, Juwhan Choi²

¹Department of Radiology, Korea University Guro Hospital, Seoul, Republic of Korea.

|September 5, 2023

Summary

This study introduces a deep learning model for segmenting the pectoralis major muscle, crucial for assessing respiratory function and diagnosing lung diseases like COPD and asthma.

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Pulmonary Medicine

Background:

The pectoralis muscle is a key indicator of respiratory muscle function.
Pectoralis muscle biomarkers correlate with parenchymal lung diseases such as asthma and COPD.
Accurate measurement of muscle volume and mass is essential for clinical applications.

Purpose of the Study:

To develop and evaluate a deep learning-based method for pectoralis major muscle segmentation.
To assess the clinical utility of automated muscle segmentation as a quantitative biomarker.

Main Methods:

A deep learning model combining muscle area detection and segmentation was developed.
The model was trained on a large dataset of 7,796 computed tomography (CT) images from 1,841 patients.
An active learning process was used to incrementally expand the training dataset.

Main Results:

The machine learning model demonstrated promising performance in segmenting the pectoralis major muscle.
High agreement was observed between automated segmentation and manual annotations by radiologists.
Training accuracy reached 0.9954, with validation loss at 0.0725 and segmentation loss at 0.0579.

Conclusions:

The developed machine learning model shows potential for accurate pectoralis major muscle segmentation.
Automated segmentation can serve as a valuable quantitative biomarker for respiratory and muscular diseases.
This technique offers potential clinical applications in diagnosing and monitoring lung conditions.